CN117434511A - Multi-target angle disambiguation method based on millimeter wave radar and related equipment - Google Patents
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Abstract
The application discloses a multi-target angle deblurring method and related equipment based on millimeter wave radar, wherein the method comprises the following steps: acquiring signals of a first uniform array and signals of a second uniform array, wherein the two uniform arrays are two groups of uniformly arranged virtual arrays in a preset MIMO array, the base lines of the two virtual arrays are in a mutual quality relationship, and the difference value of the apertures is smaller than a preset threshold value; acquiring each group of target components in a first signal space and a second signal space by using an orthogonal matching pursuit algorithm, and acquiring a group of fuzzy angles corresponding to each group of target components, wherein the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array; and (5) deblurring each group of fuzzy angles by using a spread baseline deblurring algorithm to obtain the angle of arrival of each target. The method and the device effectively solve the problem of multi-target angle ambiguity of the vehicle millimeter wave radar, and can realize high-resolution and ambiguity-free angle estimation.
Description
Technical Field
The application relates to the technical field of millimeter wave radars, in particular to a multi-target angle disambiguation method based on a millimeter wave radar and related equipment.
Background
With the rapid development of assisted/automated driving, various sensors are used for detection, sensing, modeling, etc. of environments. The millimeter wave radar has small volume, low price, no weather interference and accurate ranging and speed measurement, so the millimeter wave radar gradually becomes one of important sensors for automobile auxiliary driving/automatic driving.
For vehicle millimeter wave radar, many scholars and manufacturers have studied about the antenna layout problem in order to achieve a better angle measurement effect with fewer antennas. The traditional antenna layout adopts a uniform array, and the antenna interval is equal to half wavelength, so that the method has the advantages that no fuzzy angle measurement of plus or minus 90 degrees can be realized, but the overall aperture of the antenna is small because the number of the antennas which can be laid out by the vehicle-mounted millimeter wave radar is small, and the angle measurement performance with high resolution can not be realized by the layout method. In order to achieve the high-resolution and non-fuzzy angle measurement effect, the sparse array method becomes a hot spot for research and application.
By using sparse array combined MIMO (Multiple-Input Multiple-Output) technology, a larger antenna aperture can be achieved with fewer antennas, thereby improving the angular resolution. However, when the antenna array is sparse, the antenna spacing tends to be greater than half a wavelength, causing problems of angular ambiguity. At present, the classical methods of angular disambiguation are: the long and short baseline method, the spread baseline method, the virtual baseline method and the like are mainly used for interferometers in the past, are suitable for resolving the angle ambiguity of a single target, and are not suitable for multi-target scenes of the vehicle millimeter wave radar.
Disclosure of Invention
In view of the above, the present application provides a multi-target angle disambiguation method based on millimeter wave radar and related devices, so as to realize high-resolution, unambiguous angle estimation in vehicle-mounted applications.
In order to achieve the above object, a first aspect of the present application provides a multi-target angle disambiguation method based on millimeter wave radar, including:
acquiring signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, a base line of the first uniform array and a base line of the second uniform array are in a mutual quality relationship, and the difference value between the aperture of the first uniform array and the aperture of the second uniform array is smaller than a preset threshold value;
acquiring each group of target components in a first signal space and a second signal space by using an orthogonal matching pursuit algorithm, and acquiring a group of fuzzy angles corresponding to each group of target components, wherein the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array;
and (5) deblurring each group of fuzzy angles by using a spread baseline deblurring algorithm to obtain the angle of arrival of each target.
Preferably, the process of acquiring the signals of the first uniform array and the signals of the second uniform array includes:
acquiring original signals of each virtual array element in the MIMO array;
extracting signals of the first uniform array from the original signals based on the positions of the virtual array elements in the first uniform array;
and extracting signals of the second uniform array from the original signals based on the positions of the virtual array elements in the second uniform array.
Preferably, the process of acquiring the sets of target components in the first signal space and the second signal space using an orthogonal matching pursuit algorithm includes:
initializing a signal residual of the first signal space as a signal of the first uniform array;
initializing a signal residual of the second signal space as a signal of the second uniform array;
acquiring a first signal component with the largest contribution in the first signal space by using an orthogonal matching pursuit algorithm based on the signal residual error of the first signal space;
acquiring a second signal component with the largest contribution in the second signal space by using an orthogonal matching pursuit algorithm based on the signal residual error of the second signal space;
determining the first signal component and the second signal component as a set of target components;
Reconstructing the first signal space based on the first signal component and updating a signal residual of the first signal space;
reconstructing the second signal space based on the second signal component and updating a signal residual of the second signal space;
and returning to the step of acquiring the first signal component with the greatest contribution in the first signal space by using the orthogonal matching pursuit algorithm until the signal components of each target in the first signal space and the second signal space are acquired.
Preferably, the process of acquiring the first signal component with the largest contribution in the first signal space by using an orthogonal matching pursuit algorithm based on the signal residual of the first signal space includes:
dividing the angle measurement range equally to obtain N candidate angles, wherein N is a preset natural number;
constructing a steering vector based on each candidate angle, carrier wavelength and the position of each virtual array element in the first uniform array, wherein the steering vector comprises N components;
and acquiring a component with highest correlation degree in signal residual errors of the steering vector and the first signal space, and determining the component as a first signal component.
Preferably, the process of reconstructing the first signal space based on the first signal component and updating the signal residual of the first signal space includes:
Removing the first signal component from the first signal space to obtain a reconstructed first signal space;
and updating the signal residual error of the first signal space by using the reconstructed first signal space to obtain the signal residual error after updating the first signal space.
Preferably, the process of deblurring each set of fuzzy angles to obtain the angle of arrival of each target using a staggered baseline deblurring algorithm includes:
for each set of blur angles:
acquiring a first candidate angle of arrival set corresponding to the first uniform array and a second candidate angle of arrival set corresponding to the second uniform array based on the baseline lengths, carrier wavelengths, the preset number of candidate points and the group ambiguity angles of the first uniform array and the second uniform array;
screening a target first candidate angle of arrival from the first candidate angle of arrival set, and screening a target second candidate angle of arrival from the second candidate angle of arrival set, so that the absolute value of the difference between the target first candidate angle of arrival and the target second candidate angle of arrival is minimum;
based on the target first candidate angle of arrival and the target second candidate angle of arrival, an angle of arrival for the set of fuzzy angles is determined.
Preferably, the process of obtaining a first candidate angle of arrival set corresponding to the first uniform array and a second candidate angle of arrival set corresponding to the second uniform array based on the baseline lengths, carrier wavelengths, preset candidate point numbers, and the group ambiguity angles of the first uniform array and the second uniform array includes:
acquiring a first equivalent phase main value based on the baseline length, the carrier wavelength, the number of preset candidate points and a first fuzzy angle of the first uniform array;
acquiring a first candidate phase set based on the first equivalent phase main value, and converting each candidate phase in the first candidate phase set into a candidate angle of arrival to obtain a first candidate angle of arrival set, wherein the first fuzzy angle is a fuzzy angle corresponding to the first uniform array in the group of fuzzy angles;
acquiring a second equivalent phase main value based on the baseline length, the carrier wavelength, the number of candidate points and a second fuzzy angle of the second uniform array;
and acquiring a second candidate phase set based on the second equivalent phase main value, and converting each candidate phase in the second candidate phase set into a candidate angle of arrival to obtain a second candidate angle of arrival set, wherein the second fuzzy angle is a fuzzy angle corresponding to the second uniform array in the group of fuzzy angles.
The second aspect of the present application provides a multi-target angle deblurring device based on millimeter wave radar, comprising:
the signal acquisition unit is used for acquiring signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, a base line of the first uniform array and a base line of the second uniform array are in a mutual quality relationship, and the difference value between the aperture of the first uniform array and the aperture of the second uniform array is smaller than a preset threshold value;
a fuzzy angle acquisition unit, configured to acquire each set of target components in a first signal space and a second signal space by using an orthogonal matching pursuit algorithm, and acquire a set of fuzzy angles corresponding to each set of target components, where the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array;
and the angle defuzzification unit is used for defuzzifying each group of fuzzy angles by using a spread baseline defuzzification algorithm to obtain the arrival angle of each target.
A third aspect of the present application provides a multi-target angle disambiguation device based on millimeter wave radar, including: a memory and a processor;
The memory is used for storing programs;
the processor is used for executing the program to realize each step of the multi-target angle disambiguation method based on the millimeter wave radar.
A fourth aspect of the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a millimeter wave radar-based multi-target angular disambiguation method as described above.
According to the technical scheme, the method comprises the steps of firstly acquiring signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, and more virtual array elements can be generated by using limited antennas by designing the layout of each receiving and transmitting antenna in the array, so that the expansion of the aperture of the antenna can be realized; the base line of the first uniform array and the base line of the second uniform array are in a mutual quality relation, and the mutual quality relation is utilized so as to carry out the subsequent deblurring on the candidate angles; the difference between the apertures of the first uniform array and the apertures of the second uniform array is less than a preset threshold, i.e. the total coverage apertures of the two groups of uniform arrays are close, thus having a closer angular resolution. Then, each set of target components in a first signal space and a second signal space are acquired by using an orthogonal matching pursuit algorithm, and a set of fuzzy angles corresponding to each set of target components are acquired, wherein the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array. And finally, performing fuzzy angle solution on each group of fuzzy angles by using a fuzzy baseline solution fuzzy algorithm to obtain the angle of arrival of each target. The method provided by the application effectively solves the problem of multi-target angle ambiguity of the vehicle millimeter wave radar, and can realize high-resolution and ambiguity-free angle estimation.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
Fig. 1 is a schematic diagram of a multi-target angular disambiguation method based on millimeter wave radar disclosed in an embodiment of the present application;
FIG. 2 is a schematic workflow diagram of a simulation task result comparison method disclosed in an embodiment of the present application;
FIG. 3 illustrates signal components of 3 targets of a first signal space disclosed in an embodiment of the present application;
fig. 4 illustrates an antenna layout, a first uniform array, and a second uniform array as disclosed in an embodiment of the present application;
FIG. 5 illustrates simulation experiment calculation results under 2 targets disclosed in the embodiment of the present application;
FIG. 6 illustrates simulation experiment calculation results under 3 targets disclosed in the embodiment of the present application;
fig. 7 is a schematic diagram of a multi-target angular disambiguation device based on millimeter wave radar according to an embodiment of the present application;
Fig. 8 is a schematic diagram of a multi-target angular disambiguation device based on millimeter wave radar according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes a multi-target angle disambiguation method based on millimeter wave radar provided by the embodiment of the application. Referring to fig. 1, the multi-target angular disambiguation method based on millimeter wave radar provided in the embodiment of the present application may include the following steps:
step S101, acquiring signals of the first uniform array and signals of the second uniform array.
It can be understood that the layout of the transceiver antennas needs to be designed in advance, and under the action of the multiple transmitting antennas and the multiple receiving antennas, a MIMO array is formed, and the first uniform array and the second uniform array are two groups of virtual arrays which are uniformly arranged in the preset MIMO array. The base line of the first uniform array and the base line of the second uniform array are in a mutual quality relationship, and the difference value between the aperture of the first uniform array and the aperture of the second uniform array is smaller than a preset threshold value.
Illustratively, as shown in FIG. 2, assume that the baseline length of the first uniform matrix isThe second uniform matrix has a baseline length of +.>Then->And->The following mutual mass relationship must be satisfied:
（1）
wherein,representing a common divisor solving function,/->Representing the carrier wavelength, wherein->Indicating the speed of light +.>Representing the carrier frequency.
Step S102, each group of target components in the first signal space and the second signal space is obtained by using an orthogonal matching pursuit algorithm, and a group of fuzzy angles corresponding to each group of target components is obtained.
The orthogonal matching pursuit algorithm (Orthogonal Matching Pursuit, OMP) is one of classical algorithms in the compressed sensing field, is used as the basis of a plurality of commonly used efficient algorithms at present, and has the characteristics of simplicity and high efficiency.
The first signal space is constructed from signals of the first uniform array, and the second signal space is constructed from signals of the second uniform array. It will be appreciated that each set of target components consists of one component of the first signal space and one component of the second signal space.
Step S103, the fuzzy angle of each group is deblurred by using a fuzzy baseline deblurring algorithm, and the arrival angle of each target is obtained.
The method comprises the steps of firstly obtaining signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, and more virtual array elements can be generated by using limited antennas by designing the layout of each receiving and transmitting antenna in the array, so that the expansion of the aperture of the antenna can be realized; the base line of the first uniform array and the base line of the second uniform array are in a mutual quality relation, and the mutual quality relation is utilized so as to carry out the subsequent deblurring on the candidate angles; the difference between the apertures of the first uniform array and the apertures of the second uniform array is less than a preset threshold, i.e. the total coverage apertures of the two groups of uniform arrays are close, thus having a closer angular resolution. Then, each set of target components in a first signal space and a second signal space are acquired by using an orthogonal matching pursuit algorithm, and a set of fuzzy angles corresponding to each set of target components are acquired, wherein the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array. And finally, performing fuzzy angle solution on each group of fuzzy angles by using a fuzzy baseline solution fuzzy algorithm to obtain the angle of arrival of each target. The method provided by the application effectively solves the problem of multi-target angle ambiguity of the vehicle millimeter wave radar, and can realize high-resolution and ambiguity-free angle estimation.
In some embodiments of the present application, the process of acquiring the signal of the first uniform array and the signal of the second uniform array in step S101 may include:
s1, obtaining original signals of each virtual array element in the MIMO array.
S2, extracting signals of the first uniform array from the original signals based on the positions of the virtual array elements in the first uniform array.
S3, extracting signals of the second uniform array from the original signals based on the positions of the virtual array elements in the second uniform array.
In some embodiments of the present application, the process of acquiring each set of target components in the first signal space and the second signal space by using the orthogonal matching pursuit algorithm in step S102 may include:
s1, initializing a signal residual error of a first signal space into a signal of a first uniform array.
S2, initializing a signal residual error of the second signal space into a signal of a second uniform array.
And S3, acquiring a first signal component with the largest contribution in the first signal space by using an orthogonal matching pursuit algorithm based on the signal residual error of the first signal space.
And S4, acquiring a second signal component with the largest contribution in the second signal space by using an orthogonal matching pursuit algorithm based on the signal residual error of the second signal space.
S5, determining the first signal component and the second signal component as a group of target components.
S6, reconstructing a first signal space based on the first signal component, and updating a signal residual of the first signal space.
S7, reconstructing a second signal space based on the second signal component, and updating a signal residual of the second signal space.
S8, returning to the step S3, until the signal components of the targets in the first signal space and the second signal space are acquired.
In some embodiments of the present application, the step S3 of obtaining the first signal component with the largest contribution in the first signal space by using an orthogonal matching pursuit algorithm based on the signal residual of the first signal space includes:
s31, equally dividing the angle measurement range to obtain N candidate angles.
Wherein N is a preset natural number.
S32, constructing a first steering vector based on each candidate angle, carrier wave wavelength and the positions of virtual array elements in the first uniform array.
Wherein the first steering vector comprises N components.
And S33, acquiring a component with highest correlation degree in signal residuals of the first steering vector and the first signal space, and determining the component as a first signal component.
In some embodiments of the present application, the step S34 of obtaining the second signal component with the greatest contribution in the second signal space by using an orthogonal matching pursuit algorithm based on the signal residual of the second signal space includes:
S31, equally dividing the angle measurement range to obtain N candidate angles.
S32, constructing a second steering vector based on each candidate angle, carrier wave wavelength and the positions of virtual array elements in the second uniform array.
Wherein the second steering vector comprises N components.
And S33, acquiring a component with highest correlation degree in signal residuals of the second steering vector and the second signal space, and determining the component as a second signal component.
As is apparent from the above description, the method of signal decomposition is the same for the first signal space and the second signal space, and the process of acquiring each target component will be described in detail below by taking the first signal space as an example.
Illustratively, as shown in FIG. 3, assume a first signal spaceComposed of 3 target signal components, the target components are +.>、/>、/>。
To findThe signal component contributing the most, the angular range is first of all +.>：
；
Equally dividing into several parts to obtain N components：
；
Building a steering vector：
（2）
（3）
Wherein,is the position of each virtual array element in the first uniform array.
Then, an empty index set is establishedAnd initialize the residual +.>. And then calculating the index value of the component with the highest correlation degree with the residual error in the current signal space:
（4）
indexing the components Put index set +.>In (3), namely: />According to index->The blurring angle can be calculated as +.>. Thus, can be marked->And->The most contributing blurring angle of the current signal space is divided into +.>And->。
In some embodiments of the present application, the step S6 of reconstructing the first signal space based on the first signal component and updating the signal residual of the first signal space may include:
s61, removing the first signal component from the first signal space to obtain a reconstructed first signal space.
S62, updating the signal residual of the first signal space by using the reconstructed first signal space to obtain the signal residual after updating the first signal space.
In some embodiments of the present application, the step S7 of reconstructing the second signal space based on the second signal component and updating the signal residual of the second signal space may include:
and S71, removing the second signal component from the second signal space to obtain a reconstructed second signal space.
And S72, updating the signal residual error of the second signal space by using the reconstructed second signal space to obtain the signal residual error after updating the second signal space.
As is apparent from the above description, the spatial reconstruction and residual updating methods are the same for the first signal space and the second signal space, and the spatial reconstruction and residual updating processes will be described in detail below using the first signal space as an example.
Index set updated from the foregoingIt can be seen that the set of its corresponding signal vectors is +.>Thus, a new steering vector for the signal space can be obtained>：
；
Then updating the residual of the signal space：
（5）
In some embodiments of the present application, step S103 uses a robust baseline defuzzification algorithm to defuzzify each set of fuzzy angles to obtain the angle of arrival of each target, which may include:
for each set of blur angles:
s1, acquiring a first candidate angle of arrival set corresponding to the first uniform array and a second candidate angle of arrival set corresponding to the second uniform array based on the baseline lengths, carrier wavelengths, the preset number of candidate points and the group of fuzzy angles of the first uniform array and the second uniform array.
S2, screening a target first candidate angle of arrival from the first candidate angle of arrival set, and screening a target second candidate angle of arrival from the second candidate angle of arrival set, so that the absolute value of the difference between the target first candidate angle of arrival and the target second candidate angle of arrival is minimum.
S3, determining the arrival angles of the fuzzy angles based on the target first candidate arrival angle and the target second candidate arrival angle.
In some embodiments of the present application, the step of obtaining the first candidate angle of arrival set corresponding to the first uniform array and the second candidate angle of arrival set corresponding to the second uniform array according to the baseline lengths, the carrier wavelengths, the preset number of candidate points, and the set of fuzzy angles of the first uniform array S1 may include:
S11, acquiring a first equivalent phase main value based on the baseline length of the first uniform array, the carrier wave wavelength, the number of preset candidate points and the first fuzzy angle.
S12, a first candidate phase set is obtained based on the first equivalent phase main value, and each candidate phase in the first candidate phase set is converted into a candidate angle of arrival, so that a first candidate angle of arrival set is obtained.
Wherein the first blur angle is a blur angle of the set of blur angles corresponding to the first uniform matrix.
S13, acquiring a second equivalent phase main value based on the baseline length, the carrier wave wavelength, the number of candidate points and the second fuzzy angle of the second uniform array.
S14, a second candidate phase set is obtained based on the second equivalent phase main value, and each candidate phase in the second candidate phase set is converted into a candidate angle of arrival, so that a second candidate angle of arrival set is obtained.
Wherein the second blur angle is a blur angle of the set of blur angles corresponding to the second uniform matrix.
Illustratively, for the case ofAnd->The blurring angles corresponding to the signal components of the largest contributions obtained in the second step are known to be +.>And->. Since the baseline lengths of the 2-group uniform arrays are all greater than half wavelength, the angle +.>And->Instead of the actual angle of arrival, it needs to be deblurred, and a specific deblurring algorithm is as follows.
For angles ofAnd->The baseline of 2 uniform arrays is known to be +.>，/>First, the equivalent phase principal value is recoveredAnd->：
（6）
In the method, in the process of the invention,the number of candidate points in the range of the non-ambiguous angle measurement is represented, and the number of candidate points is +.>The larger the obtained equivalent phase principal value is, the more accurate.
And then according to the fuzzy multiple, the equivalent phase principal value is utilizedAnd->Obtaining a candidate set of phases->And->：
（7）
In the case of blur factorsAnd->Is a set of fuzzy multiples in fuzzy boundaries, the spacing being an integer of 1, the boundary value of which is related to the baseline length of the array>，/>The calculation formula of (2) is as follows:
（8）
then, to the candidate setAnd->Traversing by collectionIs phase-recovered by an angle of arrival to obtain a candidate set of angles of arrival +.>And->：
（9）
Finally, candidate set of angles of arrivalAnd->Traversing to obtain the nearest value of the arrival angles in the two groups of candidate sets, namely +.>The true angle of arrival of the ith target is thus obtained, wherein +.>Representing respective pairs->N elements and->And traversing M elements in the set to obtain the absolute value of the difference value of the N times M elements.
The technical effects of the embodiments of the present application are described below with a specific example. The antenna layout of this example is shown in FIG. 4, where the unit length is half wavelength, and the first row represents the position of the receiving antenna, respectively The second row indicates the position of the transmitting antenna, respectively +.>The third row is a MIMO arrayThe position of the column. In addition, in the third row, five-pointed star and circle represent the array element positions of two sub-uniform arrays respectively, and the base line lengths of the first uniform array and the second uniform array are +.>、/>The condition of the formula (1) is satisfied, and the total pore diameters of the two uniform arrays are +.>、/>The overall aperture size is close, and hence the angular resolution is close.
Then, according to the defuzzification method of the present application, signals of the first uniform array and the second uniform array are defuzzified. Setting 2 targets for testing in simulation experiment, wherein the target angle is as followsThe test results are shown in fig. 5. In fig. 5, the dots are the resolving result of the defuzzification method proposed in the present application, and are very close to the true angle. As can be seen from the figure, the sparse array design and the angle defuzzification method provided by the application can accurately calculate 2 targets with large angles, and the angle resolution is +.>。
FIG. 6 illustrates simulation experiment results of 3 targets, respectively. In fig. 6, the deblurring method provided by the present application can correctly calculate a plurality of target angles. Therefore, the method provided by the application can solve a plurality of targets with large angles and close angles, and can realize higher angular resolution and larger range of non-fuzzy angle measurement by using fewer array elements.
The multi-target angle disambiguation device based on the millimeter wave radar provided by the embodiment of the application is described below, and the multi-target angle disambiguation device based on the millimeter wave radar described below and the multi-target angle disambiguation method based on the millimeter wave radar described above can be referred to correspondingly.
Referring to fig. 7, the multi-target angular disambiguation device based on millimeter wave radar provided in the embodiment of the present application may include:
a signal obtaining unit 21, configured to obtain signals of a first uniform array and signals of a second uniform array, where the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, a baseline of the first uniform array and a baseline of the second uniform array are in a mutual quality relationship, and a difference value between apertures of the first uniform array and apertures of the second uniform array is smaller than a preset threshold;
a blur angle acquisition unit 22 configured to acquire respective sets of target components in a first signal space constructed from the signals of the first uniform array and a second signal space constructed from the signals of the second uniform array using an orthogonal matching pursuit algorithm, and acquire a set of blur angles corresponding to each set of target components;
An angle defuzzification unit 23, configured to defuzzify each group of fuzzy angles by using a jagged baseline defuzzification algorithm, so as to obtain the angle of arrival of each target.
In some embodiments of the present application, the process of acquiring the signal of the first uniform array and the signal of the second uniform array by the signal acquiring unit 21 may include:
acquiring original signals of each virtual array element in the MIMO array;
extracting signals of the first uniform array from the original signals based on the positions of the virtual array elements in the first uniform array;
and extracting signals of the second uniform array from the original signals based on the positions of the virtual array elements in the second uniform array.
In some embodiments of the present application, the process of the blur angle acquisition unit 22 acquiring each set of target components in the first signal space and the second signal space using the orthogonal matching pursuit algorithm may include:
initializing a signal residual of the first signal space as a signal of the first uniform array;
initializing a signal residual of the second signal space as a signal of the second uniform array;
acquiring a first signal component with the largest contribution in the first signal space by using an orthogonal matching pursuit algorithm based on the signal residual error of the first signal space;
Acquiring a second signal component with the largest contribution in the second signal space by using an orthogonal matching pursuit algorithm based on the signal residual error of the second signal space;
determining the first signal component and the second signal component as a set of target components;
reconstructing the first signal space based on the first signal component and updating a signal residual of the first signal space;
reconstructing the second signal space based on the second signal component and updating a signal residual of the second signal space;
and returning to the step of acquiring the first signal component with the greatest contribution in the first signal space by using the orthogonal matching pursuit algorithm until the signal components of each target in the first signal space and the second signal space are acquired.
In some embodiments of the present application, the process of obtaining, by the blur angle obtaining unit 22, the first signal component contributing the largest in the first signal space using the orthogonal matching pursuit algorithm based on the signal residual of the first signal space may include:
dividing the angle measurement range equally to obtain N candidate angles, wherein N is a preset natural number;
constructing a steering vector based on each candidate angle, carrier wavelength and the position of each virtual array element in the first uniform array, wherein the steering vector comprises N components;
And acquiring a component with highest correlation degree in signal residual errors of the steering vector and the first signal space, and determining the component as a first signal component.
In some embodiments of the present application, the process of reconstructing the first signal space and updating the signal residual of the first signal space by the blur angle acquisition unit 22 based on the first signal component may include:
removing the first signal component from the first signal space to obtain a reconstructed first signal space;
and updating the signal residual error of the first signal space by using the reconstructed first signal space to obtain the signal residual error after updating the first signal space.
In some embodiments of the present application, the process of the angle-of-arrival-angle-resolving unit 23 for resolving each group of fuzzy angles by using the jagged baseline-resolving fuzzy algorithm, may include:
for each set of blur angles:
acquiring a first candidate angle of arrival set corresponding to the first uniform array and a second candidate angle of arrival set corresponding to the second uniform array based on the baseline lengths, carrier wavelengths, the preset number of candidate points and the group ambiguity angles of the first uniform array and the second uniform array;
Screening a target first candidate angle of arrival from the first candidate angle of arrival set, and screening a target second candidate angle of arrival from the second candidate angle of arrival set, so that the absolute value of the difference between the target first candidate angle of arrival and the target second candidate angle of arrival is minimum;
based on the target first candidate angle of arrival and the target second candidate angle of arrival, an angle of arrival for the set of fuzzy angles is determined.
In some embodiments of the present application, the process of obtaining, by the angle deblurring unit 23, a first candidate angle of arrival set corresponding to the first uniform array and a second candidate angle of arrival set corresponding to the second uniform array based on the baseline lengths, carrier wavelengths, a preset number of candidate points, and the group of ambiguity angles of the first uniform array and the second uniform array may include:
acquiring a first equivalent phase main value based on the baseline length, the carrier wavelength, the number of preset candidate points and a first fuzzy angle of the first uniform array;
acquiring a first candidate phase set based on the first equivalent phase main value, and converting each candidate phase in the first candidate phase set into a candidate angle of arrival to obtain a first candidate angle of arrival set, wherein the first fuzzy angle is a fuzzy angle corresponding to the first uniform array in the group of fuzzy angles;
Acquiring a second equivalent phase main value based on the baseline length, the carrier wavelength, the number of candidate points and a second fuzzy angle of the second uniform array;
and acquiring a second candidate phase set based on the second equivalent phase main value, and converting each candidate phase in the second candidate phase set into a candidate angle of arrival to obtain a second candidate angle of arrival set, wherein the second fuzzy angle is a fuzzy angle corresponding to the second uniform array in the group of fuzzy angles.
The multi-target angle deblurring device based on the millimeter wave radar can be applied to multi-target angle deblurring equipment based on the millimeter wave radar, such as a computer and the like. Alternatively, fig. 8 shows a hardware configuration block diagram of a multi-target angle disambiguation device based on millimeter wave radar, and referring to fig. 8, the hardware configuration of the multi-target angle disambiguation device based on millimeter wave radar may include: at least one processor 31, at least one communication interface 32, at least one memory 33 and at least one communication bus 34.
In the embodiment of the present application, the number of the processor 31, the communication interface 32, the memory 33, and the communication bus 34 is at least one, and the processor 31, the communication interface 32, and the memory 33 complete communication with each other through the communication bus 34;
The processor 31 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present application, etc.;
the memory 33 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory) or the like, such as at least one magnetic disk memory;
wherein the memory 33 stores a program, the processor 31 may call the program stored in the memory 33, the program being for:
acquiring signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, a base line of the first uniform array and a base line of the second uniform array are in a mutual quality relationship, and the difference value between the aperture of the first uniform array and the aperture of the second uniform array is smaller than a preset threshold value;
acquiring each group of target components in a first signal space and a second signal space by using an orthogonal matching pursuit algorithm, and acquiring a group of fuzzy angles corresponding to each group of target components, wherein the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array;
And (5) deblurring each group of fuzzy angles by using a spread baseline deblurring algorithm to obtain the angle of arrival of each target.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
The embodiment of the application also provides a storage medium, which may store a program adapted to be executed by a processor, the program being configured to:
acquiring signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, a base line of the first uniform array and a base line of the second uniform array are in a mutual quality relationship, and the difference value between the aperture of the first uniform array and the aperture of the second uniform array is smaller than a preset threshold value;
acquiring each group of target components in a first signal space and a second signal space by using an orthogonal matching pursuit algorithm, and acquiring a group of fuzzy angles corresponding to each group of target components, wherein the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array;
and (5) deblurring each group of fuzzy angles by using a spread baseline deblurring algorithm to obtain the angle of arrival of each target.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
To sum up:
the method comprises the steps of firstly obtaining signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, and more virtual array elements can be generated by using limited antennas by designing the layout of each receiving and transmitting antenna in the array, so that the expansion of the aperture of the antenna can be realized; the base line of the first uniform array and the base line of the second uniform array are in a mutual quality relation, and the mutual quality relation is utilized so as to carry out the subsequent deblurring on the candidate angles; the difference between the apertures of the first uniform array and the apertures of the second uniform array is less than a preset threshold, i.e. the total coverage apertures of the two groups of uniform arrays are close, thus having a closer angular resolution. Then, each set of target components in a first signal space and a second signal space are acquired by using an orthogonal matching pursuit algorithm, and a set of fuzzy angles corresponding to each set of target components are acquired, wherein the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array. And finally, performing fuzzy angle solution on each group of fuzzy angles by using a fuzzy baseline solution fuzzy algorithm to obtain the angle of arrival of each target. The method provided by the application effectively solves the problem of multi-target angle ambiguity of the vehicle millimeter wave radar, and can realize high-resolution and ambiguity-free angle estimation.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and may be combined according to needs, and the same similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A multi-target angle disambiguation method based on millimeter wave radar is characterized by comprising the following steps:
acquiring signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, a base line of the first uniform array and a base line of the second uniform array are in a mutual quality relationship, and the difference value between the aperture of the first uniform array and the aperture of the second uniform array is smaller than a preset threshold value;
acquiring each group of target components in a first signal space and a second signal space by using an orthogonal matching pursuit algorithm, and acquiring a group of fuzzy angles corresponding to each group of target components, wherein the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array;
And (5) deblurring each group of fuzzy angles by using a spread baseline deblurring algorithm to obtain the angle of arrival of each target.
2. The method of claim 1, wherein the step of acquiring signals of the first uniform array and signals of the second uniform array comprises:
acquiring original signals of each virtual array element in the MIMO array;
extracting signals of the first uniform array from the original signals based on the positions of the virtual array elements in the first uniform array;
and extracting signals of the second uniform array from the original signals based on the positions of the virtual array elements in the second uniform array.
3. The method of claim 1, wherein the process of acquiring the sets of target components in the first signal space and the second signal space using an orthogonal matching pursuit algorithm comprises:
initializing a signal residual of the first signal space as a signal of the first uniform array;
initializing a signal residual of the second signal space as a signal of the second uniform array;
acquiring a first signal component with the largest contribution in the first signal space by using an orthogonal matching pursuit algorithm based on the signal residual error of the first signal space;
Acquiring a second signal component with the largest contribution in the second signal space by using an orthogonal matching pursuit algorithm based on the signal residual error of the second signal space;
determining the first signal component and the second signal component as a set of target components;
reconstructing the first signal space based on the first signal component and updating a signal residual of the first signal space;
reconstructing the second signal space based on the second signal component and updating a signal residual of the second signal space;
and returning to the step of acquiring the first signal component with the greatest contribution in the first signal space by using the orthogonal matching pursuit algorithm until the signal components of each target in the first signal space and the second signal space are acquired.
4. A method according to claim 3, wherein the process of obtaining the most contributing first signal component in the first signal space using an orthogonal matching pursuit algorithm based on the signal residuals of the first signal space comprises:
dividing the angle measurement range equally to obtain N candidate angles, wherein N is a preset natural number;
constructing a steering vector based on each candidate angle, carrier wavelength and the position of each virtual array element in the first uniform array, wherein the steering vector comprises N components;
And acquiring a component with highest correlation degree in signal residual errors of the steering vector and the first signal space, and determining the component as a first signal component.
5. A method according to claim 3, characterized in that the process of reconstructing the first signal space based on the first signal component and updating the signal residual of the first signal space comprises:
removing the first signal component from the first signal space to obtain a reconstructed first signal space;
and updating the signal residual error of the first signal space by using the reconstructed first signal space to obtain the signal residual error after updating the first signal space.
6. The method of claim 1, wherein the step of deblurring each set of fuzzy angles to obtain the angle of arrival for each target using a robust baseline deblurring algorithm comprises:
for each set of blur angles:
acquiring a first candidate angle of arrival set corresponding to the first uniform array and a second candidate angle of arrival set corresponding to the second uniform array based on the baseline lengths, carrier wavelengths, the preset number of candidate points and the group ambiguity angles of the first uniform array and the second uniform array;
Screening a target first candidate angle of arrival from the first candidate angle of arrival set, and screening a target second candidate angle of arrival from the second candidate angle of arrival set, so that the absolute value of the difference between the target first candidate angle of arrival and the target second candidate angle of arrival is minimum;
based on the target first candidate angle of arrival and the target second candidate angle of arrival, an angle of arrival for the set of fuzzy angles is determined.
7. The method of claim 6, wherein the process of obtaining a first set of candidate angles of arrival corresponding to the first uniform array and a second set of candidate angles of arrival corresponding to the second uniform array based on the baseline lengths of the first uniform array and the second uniform array, the carrier wavelengths, the preset number of candidate points, and the group ambiguity angles, comprises:
acquiring a first equivalent phase main value based on the baseline length, the carrier wavelength, the number of preset candidate points and a first fuzzy angle of the first uniform array;
acquiring a first candidate phase set based on the first equivalent phase main value, and converting each candidate phase in the first candidate phase set into a candidate angle of arrival to obtain a first candidate angle of arrival set, wherein the first fuzzy angle is a fuzzy angle corresponding to the first uniform array in the group of fuzzy angles;
Acquiring a second equivalent phase main value based on the baseline length, the carrier wavelength, the number of candidate points and a second fuzzy angle of the second uniform array;
and acquiring a second candidate phase set based on the second equivalent phase main value, and converting each candidate phase in the second candidate phase set into a candidate angle of arrival to obtain a second candidate angle of arrival set, wherein the second fuzzy angle is a fuzzy angle corresponding to the second uniform array in the group of fuzzy angles.
8. The utility model provides a multi-target angle deblurring device based on millimeter wave radar which characterized in that includes:
the signal acquisition unit is used for acquiring signals of a first uniform array and signals of a second uniform array, wherein the first uniform array and the second uniform array are two groups of uniformly arranged virtual arrays in a preset MIMO array, a base line of the first uniform array and a base line of the second uniform array are in a mutual quality relationship, and the difference value between the aperture of the first uniform array and the aperture of the second uniform array is smaller than a preset threshold value;
a fuzzy angle acquisition unit, configured to acquire each set of target components in a first signal space and a second signal space by using an orthogonal matching pursuit algorithm, and acquire a set of fuzzy angles corresponding to each set of target components, where the first signal space is constructed by signals of the first uniform array, and the second signal space is constructed by signals of the second uniform array;
And the angle defuzzification unit is used for defuzzifying each group of fuzzy angles by using a spread baseline defuzzification algorithm to obtain the arrival angle of each target.
9. A multi-target angular disambiguation device based on millimeter wave radar, comprising: a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the multi-target angular disambiguation method based on millimeter wave radar according to any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the millimeter wave radar-based multi-target angle disambiguation method of any one of claims 1 to 7.
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